# Deep Learning without Weight Transport

Source: [Deep Learning without Weight Transport](<https://www.sota2.com/research/paper/68fa66e41e209131cea12df4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:00:21.133Z

Authors: Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy Lillicrap, Douglas Tweed

Published: 2019-04-10T18:55:59.000Z

## Abstract

Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transport by using random feedback weights, but it performs poorly on hard visual-recognition tasks. Here we describe two mechanisms - a neural circuit called a weight mirror and a modification of an algorithm proposed by Kolen and Pollack in 1994 - both of which let the feedback path learn appropriate synaptic weights quickly and accurately even in large networks, without weight transport or complex wiring.Tested on the ImageNet visual-recognition task, these mechanisms outperform both feedback alignment and the newer sign-symmetry method, and nearly match backprop, the standard algorithm of deep learning, which uses weight transport.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.05391>)

[PDF](<https://arxiv.org/pdf/1904.05391.pdf>)